# importing required libraries
import numpy as np
import pandas as pd
import plotly

# importing csv files as pandas dataframes
races = pd.read_csv("F1_data/races.csv")
results = pd.read_csv("F1_data/results.csv")
drivers = pd.read_csv("F1_data/drivers.csv")
display(races)
display(results)
display(drivers)

	raceId	year	round	circuitId	name	date	time	url	fp1_date	fp1_time	fp2_date	fp2_time	fp3_date	fp3_time	quali_date	quali_time	sprint_date	sprint_time
0	1	2009	1	1	Australian Grand Prix	2009-03-29	06:00:00	http://en.wikipedia.org/wiki/2009_Australian_G...	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N
1	2	2009	2	2	Malaysian Grand Prix	2009-04-05	09:00:00	http://en.wikipedia.org/wiki/2009_Malaysian_Gr...	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N
2	3	2009	3	17	Chinese Grand Prix	2009-04-19	07:00:00	http://en.wikipedia.org/wiki/2009_Chinese_Gran...	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N
3	4	2009	4	3	Bahrain Grand Prix	2009-04-26	12:00:00	http://en.wikipedia.org/wiki/2009_Bahrain_Gran...	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N
4	5	2009	5	4	Spanish Grand Prix	2009-05-10	12:00:00	http://en.wikipedia.org/wiki/2009_Spanish_Gran...	\N	\N	\N	\N	\N	\N	\N	\N	\N	\N
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1074	1092	2022	18	22	Japanese Grand Prix	2022-10-09	05:00:00	http://en.wikipedia.org/wiki/2022_Japanese_Gra...	2022-10-07	04:00:00	2022-10-07	08:00:00	2022-10-08	04:00:00	2022-10-08	07:00:00	\N	\N
1075	1093	2022	19	69	United States Grand Prix	2022-10-23	19:00:00	http://en.wikipedia.org/wiki/2022_United_State...	2022-10-21	19:00:00	2022-10-21	22:00:00	2022-10-22	19:00:00	2022-10-22	22:00:00	\N	\N
1076	1094	2022	20	32	Mexico City Grand Prix	2022-10-30	20:00:00	http://en.wikipedia.org/wiki/2022_Mexican_Gran...	2022-10-28	18:00:00	2022-10-28	21:00:00	2022-10-29	17:00:00	2022-10-29	20:00:00	\N	\N
1077	1095	2022	21	18	Brazilian Grand Prix	2022-11-13	18:00:00	http://en.wikipedia.org/wiki/2022_Brazilian_Gr...	2022-11-11	15:30:00	2022-11-12	15:30:00	\N	\N	2022-11-11	19:00:00	2022-11-12	19:30:00
1078	1096	2022	22	24	Abu Dhabi Grand Prix	2022-11-20	13:00:00	http://en.wikipedia.org/wiki/2022_Abu_Dhabi_Gr...	2022-11-18	09:00:00	2022-11-18	12:00:00	2022-11-19	10:00:00	2022-11-19	13:00:00	\N	\N

1079 rows × 18 columns

	resultId	raceId	driverId	constructorId	number	grid	position	positionText	positionOrder	points	laps	time	milliseconds	fastestLap	rank	fastestLapTime	fastestLapSpeed	statusId
0	1	18	1	1	22	1	1	1	1	10.0	58	1:34:50.616	5690616	39	2	1:27.452	218.300	1
1	2	18	2	2	3	5	2	2	2	8.0	58	+5.478	5696094	41	3	1:27.739	217.586	1
2	3	18	3	3	7	7	3	3	3	6.0	58	+8.163	5698779	41	5	1:28.090	216.719	1
3	4	18	4	4	5	11	4	4	4	5.0	58	+17.181	5707797	58	7	1:28.603	215.464	1
4	5	18	5	1	23	3	5	5	5	4.0	58	+18.014	5708630	43	1	1:27.418	218.385	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
25655	25661	1086	825	210	20	13	16	16	16	0.0	69	\N	\N	37	15	1:23.511	188.856	11
25656	25662	1086	848	3	23	17	17	17	17	0.0	69	\N	\N	43	12	1:23.047	189.911	11
25657	25663	1086	849	3	6	19	18	18	18	0.0	69	\N	\N	60	8	1:22.478	191.221	11
25658	25664	1086	852	213	22	16	19	19	19	0.0	68	\N	\N	58	16	1:23.538	188.795	12
25659	25665	1086	822	51	77	8	20	20	20	0.0	65	\N	\N	60	19	1:24.002	187.752	131

25660 rows × 18 columns

	driverId	driverRef	number	code	forename	surname	dob	nationality	url
0	1	hamilton	44	HAM	Lewis	Hamilton	1985-01-07	British	http://en.wikipedia.org/wiki/Lewis_Hamilton
1	2	heidfeld	\N	HEI	Nick	Heidfeld	1977-05-10	German	http://en.wikipedia.org/wiki/Nick_Heidfeld
2	3	rosberg	6	ROS	Nico	Rosberg	1985-06-27	German	http://en.wikipedia.org/wiki/Nico_Rosberg
3	4	alonso	14	ALO	Fernando	Alonso	1981-07-29	Spanish	http://en.wikipedia.org/wiki/Fernando_Alonso
4	5	kovalainen	\N	KOV	Heikki	Kovalainen	1981-10-19	Finnish	http://en.wikipedia.org/wiki/Heikki_Kovalainen
...	...	...	...	...	...	...	...	...	...
849	851	aitken	89	AIT	Jack	Aitken	1995-09-23	British	http://en.wikipedia.org/wiki/Jack_Aitken
850	852	tsunoda	22	TSU	Yuki	Tsunoda	2000-05-11	Japanese	http://en.wikipedia.org/wiki/Yuki_Tsunoda
851	853	mazepin	9	MAZ	Nikita	Mazepin	1999-03-02	Russian	http://en.wikipedia.org/wiki/Nikita_Mazepin
852	854	mick_schumacher	47	MSC	Mick	Schumacher	1999-03-22	German	http://en.wikipedia.org/wiki/Mick_Schumacher
853	855	zhou	24	ZHO	Guanyu	Zhou	1999-05-30	Chinese	http://en.wikipedia.org/wiki/Guanyu_Zhou

854 rows × 9 columns

Question 1

What are the maximum number of wins by a single driver during each F1 season?

# creating subsets of columns with the required columns
races_subset = races[['raceId', 'year']]
display(races_subset.raceId.unique())
display(races_subset.year.unique())

array([   1,    2,    3, ..., 1094, 1095, 1096], dtype=int64)

array([2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999,
       1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 2010, 1989,
       1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979, 1978,
       1977, 1976, 1975, 1974, 1973, 1972, 1971, 1970, 1969, 1968, 1967,
       1966, 1965, 1964, 1963, 1962, 1961, 1960, 1959, 1958, 1957, 1956,
       1955, 1954, 1953, 1952, 1951, 1950, 2011, 2012, 2013, 2014, 2015,
       2016, 2017, 2018, 2019, 2020, 2021, 2022], dtype=int64)

# creating subsets of columns with the required columns
results_subset = results[['resultId','driverId','raceId','positionText']]
display(results_subset.resultId.unique())
display(results_subset.driverId.unique())
display(results_subset.raceId.unique())
display(results_subset.positionText.unique())

array([    1,     2,     3, ..., 25663, 25664, 25665], dtype=int64)

array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,
        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,
        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,
        53,  56,  63,  62,  59,  66,  54,  55,  57,  58,  60,  61,  64,
        65,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,
        80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,
        93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105,
       106, 107, 108, 110, 109, 111, 112, 113, 114, 115, 116, 117, 118,
       119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,
       132, 135, 136, 137, 138, 139, 133, 140, 141, 142, 143, 144, 145,
       146, 147, 148, 151, 149, 150, 152,  67, 153, 154, 155, 156, 157,
       158, 159, 163, 160, 161, 162, 164, 134, 165, 166, 167, 168, 169,
       170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,
       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,
       196, 197, 198, 199, 200, 206, 201, 202, 203, 204, 205, 207, 208,
       209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,
       222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,
       235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
       248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,
       261, 262, 263, 267, 264, 265, 266, 268, 269, 270, 271, 272, 273,
       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,
       287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,
       300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,
       314, 313, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,
       326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,
       339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,
       352, 353, 354, 355, 356, 357, 362, 358, 359, 360, 361, 363, 364,
       365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377,
       378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390,
       391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403,
       404, 405, 406, 408, 407, 409, 410, 411, 412, 413, 414, 415, 416,
       417, 418, 420, 419, 421, 422, 423, 424, 425, 426, 427, 428, 429,
       430, 431, 432, 433, 434, 435, 440, 436, 437, 438, 439, 441, 442,
       443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455,
       456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468,
       469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481,
       482, 483, 485, 484, 487, 488, 489, 490, 486, 491, 492, 493, 494,
       495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507,
       508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520,
       521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533,
       534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546,
       547, 548, 554, 549, 550, 551, 552, 553, 555, 556, 557, 558, 559,
       560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572,
       573, 574, 575, 576, 577, 578, 579, 580, 581, 590, 582, 583, 584,
       585, 586, 587, 588, 589, 591, 592, 593, 594, 595, 596, 597, 598,
       599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611,
       612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624,
       625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637,
       638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650,
       651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663,
       664, 666, 665, 667, 668, 669, 670, 671, 672, 673, 674, 675, 679,
       676, 677, 678, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689,
       690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702,
       703, 704, 706, 707, 708, 705, 710, 709, 711, 712, 713, 714, 715,
       716, 717, 718, 720, 719, 721, 722, 723, 725, 724, 726, 727, 728,
       729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 741, 740,
       742, 743, 744, 745, 746, 747, 748, 749, 751, 752, 753, 750, 754,
       755, 756, 757, 758, 759, 762, 761, 760, 763, 764, 765, 766, 767,
       768, 769, 770, 771, 772, 773, 786, 774, 775, 778, 776, 777, 779,
       780, 781, 783, 782, 787, 784, 785, 789, 790, 793, 792, 794, 795,
       796, 797, 798, 799, 800, 801, 802, 791, 788, 803, 804, 805, 806,
       807, 811, 808, 810, 812, 814, 816, 813, 815, 817, 818, 819, 821,
       822, 824, 820, 823, 825, 826, 828, 827, 829, 831, 832, 830, 833,
       834, 835, 836, 837, 838, 839, 841, 840, 842, 843, 844, 845, 846,
       848, 847, 849, 851, 850, 852, 854, 853, 855], dtype=int64)

array([  18,   19,   20, ..., 1084, 1085, 1086], dtype=int64)

array(['1', '2', '3', '4', '5', '6', '7', '8', 'R', 'D', '9', '10', '11',
       '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', 'N',
       'W', 'F', 'E', '22', '23', '24', '25', '26', '27', '28', '29',
       '30', '31', '32', '33'], dtype=object)

# Cleaning result file 
results_cleaned=results_subset.loc[(results_subset['positionText'] != 'R') & (results_subset['positionText'] != 'N' )
                                       & (results_subset['positionText'] != 'E' ) & (results_subset['positionText'] != 'W' )& (results_subset['positionText'] != 'D')
                                                                                                                              & (results_subset['positionText'] != 'F' )]
display(results_cleaned.positionText.unique())

array(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',
       '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23',
       '24', '25', '26', '27', '28', '29', '30', '31', '32', '33'],
      dtype=object)

# Converting Dtype Object -> int
results_cleaned = results_cleaned.astype({'positionText':'int'})
print(results_cleaned.dtypes)
display(results_cleaned.positionText.unique())

resultId        int64
driverId        int64
raceId          int64
positionText    int32
dtype: object

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33])

## creating subsets of columns with the required columns
drivers_subset = drivers[['driverId', 'forename','surname']]
display(drivers_subset.driverId.unique())
display(drivers_subset.forename.unique())
display(drivers_subset.surname.unique())

array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,
        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,
        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,
        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,
        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,
        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,
        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,
       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,
       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,
       131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,
       144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,
       157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,
       170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,
       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,
       196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,
       209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,
       222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,
       235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
       248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,
       261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,
       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,
       287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,
       300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,
       313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,
       326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,
       339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,
       352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,
       365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377,
       378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390,
       391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403,
       404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416,
       417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429,
       430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442,
       443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 842, 453, 454,
       455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,
       468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,
       481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,
       494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,
       507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,
       520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532,
       533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,
       546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558,
       559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571,
       572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584,
       585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597,
       598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610,
       611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623,
       624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636,
       637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649,
       650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662,
       663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675,
       676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688,
       689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701,
       702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714,
       715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727,
       728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740,
       741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753,
       754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766,
       767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779,
       780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792,
       793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805,
       806, 807, 808, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819,
       820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832,
       833, 834, 835, 836, 837, 838, 839, 840, 841, 843, 844, 845, 846,
       847, 848, 849, 850, 851, 852, 853, 854, 855], dtype=int64)

array(['Lewis', 'Nick', 'Nico', 'Fernando', 'Heikki', 'Kazuki',
       'Sébastien', 'Kimi', 'Robert', 'Timo', 'Takuma', 'Nelson',
       'Felipe', 'David', 'Jarno', 'Adrian', 'Mark', 'Jenson', 'Anthony',
       'Sebastian', 'Giancarlo', 'Rubens', 'Ralf', 'Vitantonio',
       'Alexander', 'Scott', 'Christijan', 'Markus', 'Sakon', 'Michael',
       'Juan', 'Christian', 'Tiago', 'Yuji', 'Jacques', 'Franck', 'Pedro',
       'Narain', 'Patrick', 'Ricardo', 'Antônio', 'Cristiano', 'Olivier',
       'Giorgio', 'Gianmaria', 'Zsolt', 'Marc', 'Heinz-Harald', 'Jos',
       'Justin', 'Ralph', 'Nicolas', 'Luciano', 'Jean', 'Eddie', 'Mika',
       'Tarso', 'Enrique', 'Gastón', 'Tomáš', 'Alex', 'Johnny', 'Allan',
       'Toranosuke', 'Luca', 'Alessandro', 'Damon', 'Stéphane', 'Esteban',
       'Shinji', 'Jan', 'Gerhard', 'Nicola', 'Ukyo', 'Vincenzo', 'Gianni',
       'Norberto', 'Martin', 'Andrea', 'Giovanni', 'Aguri', 'Taki',
       'Roberto', 'Karl', 'Bertrand', 'Domenico', 'Pierluigi', 'Nigel',
       'Jean-Christophe', 'Massimiliano', 'Jean-Denis', 'Gabriele',
       'Érik', 'Ayrton', 'Éric', 'Michele', 'Roland', 'Paul', 'Jyrki',
       'Jean-Marc', 'Philippe', 'Yannick', 'Hideki', 'Alain', 'Derek',
       'Riccardo', 'Fabrizio', 'Ivan', 'Thierry', 'Marco', 'Emanuele',
       'Toshio', 'Maurício', 'Eric', 'Stefano', 'Giovanna', 'Enrico',
       'Perry', 'Satoru', 'Stefan', 'Julian', 'Naoki', 'Bernd', 'Paolo',
       'Gregor', 'Claudio', 'Gary', 'Bruno', 'Jaime', 'Romain', 'Kamui',
       'Jonathan', 'Luis', 'Piercarlo', 'Volker', 'Pierre-Henri', 'René',
       'Joachim', 'Oscar', 'Adrián', 'Jean-Louis', 'Pascal', 'Teo',
       'Franco', 'Elio', 'Keke', 'Alan', 'Huub', 'Allen', 'Manfred',
       'Niki', 'François', 'Mauro', 'Kenny', 'John', 'Jo', 'Corrado',
       'Mike', 'Chico', 'Danny', 'Eliseo', 'Raul', 'Jean-Pierre',
       'Carlos', 'Jochen', 'Slim', 'Didier', 'Gilles', 'Brian', 'Mario',
       'Emilio', 'Geoff', 'Tommy', 'Rupert', 'Hector', 'Beppe', 'Kevin',
       'Miguel Ángel', 'Siegfried', 'Jody', 'Clay', 'Emerson', 'Dave',
       'Stephen', 'Tiff', 'Desiré', 'Harald', 'Vittorio', 'James',
       'Arturo', 'Hans-Joachim', 'Gianfranco', 'Jacky', 'Ronnie', 'Brett',
       'Lamberto', 'Divina', 'Rolf', 'Alberto', 'Tony', 'Hans', 'Carlo',
       'Bobby', 'Ian', 'Tom', 'Ingo', 'Renzo', 'Gunnar', 'Larry', 'Boy',
       'Conny', 'Bernard', 'Jackie', 'Mikko', 'Andy', 'Guy', 'Vern',
       'Teddy', 'Loris', 'Kunimitsu', 'Kazuyoshi', 'Noritake', 'Lella',
       'Bob', 'Michel', 'Chris', 'Henri', 'Jac', 'Damien', 'Otto',
       'Warwick', 'Masahiro', 'Graham', 'Wilson', 'Roelof', 'Torsten',
       'Gijs', 'Hiroshi', 'Jim', 'Denny', 'Howden', 'Richard', 'Peter',
       'Paddy', 'Rikky', 'Tim', 'Gérard', 'Leo', 'Reine', 'Bertil',
       'José', 'Dieter', 'Helmuth', 'Eppie', 'Nanni', 'Luiz', 'George',
       'Roger', 'Helmut', 'Skip', 'Bill', 'Sam', 'Max', 'Vic', 'Silvio',
       'Pete', 'Jack', 'Bruce', 'Piers', 'Ignazio', 'Dan', 'Hubert',
       'Gus', 'Basil', 'Al', 'Ludovico', 'Lucien', 'Robin', 'Kurt',
       'Frank', 'Moisés', 'Luki', 'Lorenzo', 'Richie', 'Trevor',
       'Giacomo', 'Phil', 'Innes', 'Neville', 'Doug', 'Brausch', 'Ernie',
       'Clive', 'Ray', 'Masten', 'Nino', 'Maurice', 'André',
       'Carel Godin', 'Edgar', 'Mário de Araújo', 'Walt', 'Hap', 'Willy',
       'Nasif', 'Ernesto', 'Günther', 'Gaetano', 'Rodger', 'Thomas',
       'Pierre', 'Wolfgang', 'Roy', 'Ben', 'Rob', 'Gerry', 'Heinz',
       'Colin', 'Jay', 'Keith', 'Heini', 'Timmy', 'Syd', 'Stirling',
       'Cliff', 'Henry', 'Juan Manuel', 'Massimo', 'Renato', 'Alfonso',
       'Menato', 'Lloyd', 'Ken', 'Alberto Rodriguez', 'José Froilán',
       'Gino', 'Harry', 'Ettore', 'Antonio', 'Chuck', 'Lance', 'Don',
       'Bud', 'Red', 'Duane', 'Gene', 'Shorty', 'Jimmy', 'Troy', 'Wayne',
       'Len', 'Dick', 'Dempsey', 'Ron', 'Giulio', 'Piero', 'Fred',
       'Arthur', 'Horace', 'Ivor', 'Maria', 'Pat', 'Jud', 'Carroll',
       'Fritz', 'Azdrubal', 'Dennis', 'Luigi', 'Paco', 'Gerino', 'Bernie',
       'Louis', 'Stuart', 'Johnnie', 'Billy', 'Ed', 'Jerry', 'Art',
       'Cesare', 'Eugenio', 'Les', 'Marshall', 'Elmer', 'Herbert',
       'Umberto', 'Hernando', 'Élie', 'Duke', 'Desmond', 'Archie',
       'Ottorino', 'Toulo', 'Sergio', 'Clemar', 'Jesús', 'Pablo', 'Ted',
       'Cal', 'Kenneth', 'Leslie', 'Prince', 'Onofre', 'Jorge', 'Manny',
       'Travis', 'Georges', 'Reg', 'Rodney', 'Hermann', 'Theo', 'Felice',
       'Adolfo', 'Carl', 'Yves', 'Duncan', 'Ernst', 'Rudolf', 'Oswald',
       'Willi', 'Erwin', 'Albert', 'Rudi', 'Toni', 'Joe', 'Chet',
       'Charles', 'Eitel', 'Adolf', 'Marcel', 'Josef', 'Dries',
       'Consalvo', 'Lee', 'Mauri', 'Cecil', 'Mack', 'Eugène', 'Aldo',
       'Philip', 'Cuth', 'Clemente', 'Alfredo', 'Raymond', 'Joie',
       'Myron', 'Bayliss', 'Nello', 'Dorino', 'Óscar', 'Vitaly', 'Lucas',
       'Karun', 'Pastor', 'Jérôme', 'Daniel', 'Jean-Éric', 'Valtteri',
       'Giedo', 'Jules', 'Daniil', 'Marcus', 'Will', 'Jolyon', 'Rio',
       'Stoffel', 'Brendon', 'Sergey', 'Lando', 'Nicholas', 'Pietro',
       'Yuki', 'Nikita', 'Mick', 'Guanyu'], dtype=object)

array(['Hamilton', 'Heidfeld', 'Rosberg', 'Alonso', 'Kovalainen',
       'Nakajima', 'Bourdais', 'Räikkönen', 'Kubica', 'Glock', 'Sato',
       'Piquet Jr.', 'Massa', 'Coulthard', 'Trulli', 'Sutil', 'Webber',
       'Button', 'Davidson', 'Vettel', 'Fisichella', 'Barrichello',
       'Schumacher', 'Liuzzi', 'Wurz', 'Speed', 'Albers', 'Winkelhock',
       'Yamamoto', 'Pablo Montoya', 'Klien', 'Monteiro', 'Ide',
       'Villeneuve', 'Montagny', 'de la Rosa', 'Doornbos', 'Karthikeyan',
       'Friesacher', 'Zonta', 'Pizzonia', 'da Matta', 'Panis', 'Pantano',
       'Bruni', 'Baumgartner', 'Gené', 'Frentzen', 'Verstappen', 'Wilson',
       'Firman', 'Kiesa', 'Burti', 'Alesi', 'Irvine', 'Häkkinen',
       'Marques', 'Bernoldi', 'Mazzacane', 'Enge', 'Yoong', 'Salo',
       'Diniz', 'Herbert', 'McNish', 'Buemi', 'Takagi', 'Badoer',
       'Zanardi', 'Hill', 'Sarrazin', 'Rosset', 'Tuero', 'Nakano',
       'Magnussen', 'Berger', 'Larini', 'Katayama', 'Sospiri',
       'Morbidelli', 'Fontana', 'Lamy', 'Brundle', 'Montermini',
       'Lavaggi', 'Blundell', 'Suzuki', 'Inoue', 'Moreno', 'Wendlinger',
       'Gachot', 'Schiattarella', 'Martini', 'Mansell', 'Boullion',
       'Papis', 'Délétraz', 'Tarquini', 'Comas', 'Brabham', 'Senna',
       'Bernard', 'Fittipaldi', 'Alboreto', 'Beretta', 'Ratzenberger',
       'Belmondo', 'Järvilehto', 'de Cesaris', 'Gounon', 'Alliot',
       'Adams', 'Dalmas', 'Noda', 'Lagorce', 'Prost', 'Warwick',
       'Patrese', 'Barbazza', 'Andretti', 'Capelli', 'Boutsen',
       'Apicella', 'Naspetti', 'Gugelmin', 'van de Poele', 'Grouillard',
       'Chiesa', 'Modena', 'Amati', 'Caffi', 'Bertaggia', 'McCarthy',
       'Lammers', 'Piquet', 'Pirro', 'Johansson', 'Bailey', 'Chaves',
       'Bartels', 'Hattori', 'Nannini', 'Schneider', 'Barilla', 'Foitek',
       'Langes', 'Donnelly', 'Giacomelli', 'Alguersuari', 'Grosjean',
       'Kobayashi', 'Palmer', 'Danner', 'Cheever', 'Pérez-Sala',
       'Ghinzani', 'Weidler', 'Raphanel', 'Arnoux', 'Larrauri', 'Streiff',
       'Campos', 'Schlesser', 'Fabre', 'Fabi', 'Forini', 'Laffite',
       'de Angelis', 'Dumfries', 'Tambay', 'Surer', 'Jones',
       'Rothengatter', 'Berg', 'Lauda', 'Hesnault', 'Baldi', 'Bellof',
       'Acheson', 'Watson', 'Cecotto', 'Gartner', 'Thackwell', 'Serra',
       'Sullivan', 'Salazar', 'Guerrero', 'Boesel', 'Jarier',
       'Villeneuve Sr.', 'Reutemann', 'Mass', 'Borgudd', 'Pironi',
       'Paletti', 'Henton', 'Daly', 'de Villota', 'Lees', 'Byrne',
       'Keegan', 'Rebaque', 'Gabbiani', 'Cogan', 'Guerra', 'Stohr',
       'Zunino', 'Londoño', 'Jabouille', 'Francia', 'Depailler',
       'Scheckter', 'Regazzoni', 'Kennedy', 'South', 'Needell', 'Ertl',
       'Brambilla', 'Hunt', 'Merzario', 'Stuck', 'Brancatelli', 'Ickx',
       'Gaillard', 'Ribeiro', 'Peterson', 'Lunger', 'Ongais', 'Leoni',
       'Galica', 'Stommelen', 'Colombo', 'Trimmer', 'Binder',
       'Bleekemolen', 'Franchi', 'Rahal', 'Pace', 'Pryce', 'Hoffmann',
       'Zorzi', 'Nilsson', 'Perkins', 'Nève', 'Purley', 'Andersson',
       'de Dryver', 'Oliver', 'Kozarowitzky', 'Sutcliffe', 'Edwards',
       'McGuire', 'Schuppan', 'Heyer', 'Pilette', 'Ashley', 'Kessel',
       'Takahashi', 'Hoshino', 'Takahara', 'Lombardi', 'Evans', 'Leclère',
       'Amon', 'Zapico', 'Pescarolo', 'Nelleman', 'Magee', 'Wilds',
       'Pesenti-Rossi', 'Stuppacher', 'Brown', 'Hasemi', 'Donohue',
       'Tunmer', 'Keizan', 'Charlton', 'Brise', 'Wunderink', 'Migault',
       'Palm', 'van Lennep', 'Fushida', 'Nicholson', 'Morgan', 'Crawford',
       'Vonlanthen', 'Hulme', 'Hailwood', 'Beltoise', 'Ganley', 'Robarts',
       'Revson', 'Driver', 'Belsø', 'Redman', 'von Opel', 'Schenken',
       'Larrousse', 'Kinnunen', 'Wisell', 'Roos', 'Dolhem', 'Gethin',
       'Bell', 'Hobbs', 'Quester', 'Koinigg', 'Facetti', 'Wietzes',
       'Cevert', 'Stewart', 'Beuttler', 'Galli', 'Bueno', 'Follmer',
       'de Adamich', 'Pretorius', 'Williamson', 'McRae', 'Marko',
       'Walker', 'Soler-Roig', 'Love', 'Surtees', 'Barber', 'Brack',
       'Posey', 'Rodríguez', 'Siffert', 'Bonnier', 'Mazet', 'Jean',
       'Elford', 'Moser', 'Eaton', 'Lovely', 'Craft', 'Cannon', 'Miles',
       'Rindt', 'Servoz-Gavin', 'McLaren', 'Courage', 'de Klerk',
       'Giunti', 'Gurney', 'Hahne', 'Hutchison', 'Westbury', 'Tingle',
       'van Rooyen', 'Attwood', 'Pease', 'Cordts', 'Clark', 'Spence',
       'Scarfiotti', 'Bianchi', 'Widdows', 'Ahrens', 'Gardner', 'Unser',
       'Solana', 'Anderson', 'Botha', 'Bandini', 'Ginther', 'Parkes',
       'Irwin', 'Ligier', 'Rees', 'Hart', 'Fisher', 'Baghetti',
       'Williams', 'Bondurant', 'Arundell', 'Taylor', 'Lawrence', 'Russo',
       'Ireland', 'Bucknum', 'Hawkins', 'Prophet', 'Maggs', 'Blokdyk',
       'Lederle', 'Serrurier', 'Niemann', 'Pieterse', 'Puzey', 'Reed',
       'Clapham', 'Blignaut', 'Gregory', 'Rhodes', 'Raby', 'Rollinson',
       'Gubby', 'Mitter', 'Bussinello', 'Vaccarella', 'Bassi',
       'Trintignant', 'Collomb', 'de Beaufort', 'Barth', 'Cabral',
       'Hansgen', 'Sharp', 'Mairesse', 'Campbell-Jones', 'Burgess',
       'Settember', 'Estéfano', 'Hall', 'Parnell', 'Kuhnke', 'Lippi',
       'Seiffert', 'Abate', 'Starrabba', 'Broeker', 'Ward', 'de Vos',
       'Dochnal', 'Monarch', 'Gasly', 'Lewis', 'Seidel', 'Salvadori',
       'Pon', 'Slotemaker', 'Marsh', 'Ashmore', 'Schiller', 'Davis',
       'Chamberlain', 'Shelly', 'Greene', 'Walter', 'Prinoth', 'Penske',
       'Schroeder', 'Mayer', 'Johnstone', 'Harris', 'Hocking',
       'van der Vyver', 'Moss', 'von Trips', 'Allison', 'Herrmann',
       'Brooks', 'May', 'Gendebien', 'Scarlatti', 'Naylor', 'Bordeu',
       'Fairman', 'Natili', 'Monteverdi', 'Pirocchi', 'Duke', 'Thiele',
       'Boffa', 'Ryan', 'Ruby', 'Menditeguy', 'Larreta', 'González',
       'Bonomi', 'Munaron', 'Schell', 'Stacey', 'Chimeri', 'Creus',
       'Bristow', 'Halford', 'Daigh', 'Reventlow', 'Rathmann',
       'Goldsmith', 'Branson', 'Thomson', 'Johnson', 'Veith',
       'Tingelstad', 'Christie', 'Amick', 'Carter', 'Homeier', 'Hartley',
       'Stevenson', 'Grim', 'Templeman', 'Hurtubise', 'Bryan', 'Ruttman',
       'Sachs', 'Freeland', 'Bettenhausen', 'Weiler', 'Foyt', 'Boyd',
       'Force', 'McWithey', 'Sutton', 'Herman', 'Flockhart', 'Piper',
       'Cabianca', 'Drogo', 'Gamble', 'Owen', 'Gould', 'Drake', 'Bueb',
       'de Changy', 'de Filippis', 'Lucienbonnet', 'Testut', 'Behra',
       'Daywalt', 'Arnold', 'Keller', 'Flaherty', 'Cheesbourg', 'Turner',
       'Weyant', 'Larson', 'Magill', 'Shelby', "d'Orey", 'Fontes',
       'Ashdown', 'Blanchard', 'de Tomaso', 'Constantine', 'Said', 'Cade',
       'Musso', 'Hawthorn', 'Fangio', 'Godia', 'Collins', 'Kavanagh',
       'Gerini', 'Kessler', 'Emery', 'Piotti', 'Ecclestone', 'Taramazzo',
       'Chiron', 'Lewis-Evans', 'Reece', 'Parsons', 'Tolan', 'Garrett',
       'Elisian', "O'Connor", 'Bisch', 'Goethals', 'Gibson', 'La Caze',
       'Guelfi', 'Picard', 'Bridger', 'de Portago', 'Perdisa',
       'Castellotti', 'Simon', 'Leston', 'Hanks', 'Linden', 'Teague',
       'Edmunds', 'Agabashian', 'George', 'MacDowel', 'MacKay-Fraser',
       'Gerard', 'Maglioli', 'England', 'Landi', 'Uria', 'da Silva Ramos',
       'Bayol', 'Manzon', 'Rosier', 'Sweikert', 'Griffith', 'Dinsmore',
       'Andrews', 'Frère', 'Villoresi', 'Scotti', 'Chapman',
       'Titterington', 'Scott Brown', 'Volonterio', 'Milhoux',
       'de Graffenried', 'Taruffi', 'Farina', 'Mieres', 'Mantovani',
       'Bucci', 'Iglesias', 'Ascari', 'Kling', 'Birger', 'Pollet',
       'Macklin', 'Whiteaway', 'Davies', 'Faulkner', 'Niday', 'Cross',
       'Vukovich', 'McGrath', 'Hoyt', 'Claes', 'Sparken', 'Wharton',
       'McAlpine', 'Marr', 'Rolt', 'Fitch', 'Lucas', 'Bira', 'Marimón',
       'Loyer', 'Daponte', 'Nazaruk', 'Crockett', 'Ayulo', 'Armi', 'Webb',
       'Duncan', 'McCoy', 'Swaters', 'Beauman', 'Thorne', 'Whitehouse',
       'Riseley-Prichard', 'Whitehead', 'Brandon', 'Nuckey', 'Lang',
       'Helfrich', 'Wacker', 'de Riu', 'Gálvez', 'Bonetto', 'Cruz',
       'Nalon', 'Scarborough', 'Holland', 'Scott', 'Legat', 'Cabantous',
       'Crook', 'Klodwig', 'Krause', 'Karch', 'Heeks', 'Fitzau', 'Adolff',
       'Bechem', 'Bauer', 'von Stuck', 'Loof', 'Scherrer', 'de Terra',
       'Hirt', 'Carini', 'Fischer', 'Ulmen', 'Abecassis', 'Connor',
       'Rigsby', 'James', 'Schindler', 'Fonder', 'Banks', 'McDowell',
       'Miller', 'Ball', 'de Tornaco', 'Laurent', "O'Brien", 'Gaze',
       'Montgomerie-Charrington', 'Comotti', 'Étancelin', 'Poore',
       'Thompson', 'Downing', 'Bianco', 'Murray', 'Cantoni', 'Aston',
       'Brudes', 'Riess', 'Niedermayr', 'Klenk', 'Balsa', 'Schoeller',
       'Pietsch', 'Peters', 'van der Lof', 'Flinterman', 'Dusio',
       'Crespo', 'Rol', 'Sanesi', 'Louveau', 'Wallard', 'Forberg', 'Rose',
       'Mackey', 'Green', 'Hellings', 'Levegh', 'Chaboud', 'Gordini',
       'Kelly', 'Fotheringham-Parker', 'Shawe Taylor', 'Branca',
       'Richardson', 'Jover', 'Grignard', 'Hampshire', 'Crossley',
       'Fagioli', 'Harrison', 'Fry', 'Martin', 'Biondetti', 'Pián',
       'Sommer', 'Chitwood', 'Fohr', 'Ader', 'Holmes', 'Levrett',
       'Jackson', 'Pagani', 'Pozzi', 'Serafini', 'Cantrell', 'Mantz',
       'Kladis', 'Hülkenberg', 'Petrov', 'di Grassi', 'Chandhok',
       'Maldonado', 'di Resta', 'Pérez', "d'Ambrosio", 'Ricciardo',
       'Vergne', 'Pic', 'Chilton', 'Gutiérrez', 'Bottas', 'van der Garde',
       'Kvyat', 'Lotterer', 'Ericsson', 'Stevens', 'Nasr', 'Sainz',
       'Merhi', 'Rossi', 'Wehrlein', 'Haryanto', 'Vandoorne', 'Ocon',
       'Stroll', 'Giovinazzi', 'Leclerc', 'Sirotkin', 'Norris', 'Russell',
       'Albon', 'Latifi', 'Aitken', 'Tsunoda', 'Mazepin', 'Zhou'],
      dtype=object)

## joining the 2 datasets to create a final database for analysis
Results_races_combined=pd.merge(results_cleaned,races_subset,on='raceId',how='left')
Final_Dataset=pd.merge(Results_races_combined,drivers_subset,on='driverId',how='left')
display(Final_Dataset)

	resultId	driverId	raceId	positionText	year	forename	surname
0	1	1	18	1	2008	Lewis	Hamilton
1	2	2	18	2	2008	Nick	Heidfeld
2	3	3	18	3	2008	Nico	Rosberg
3	4	4	18	4	2008	Fernando	Alonso
4	5	5	18	5	2008	Heikki	Kovalainen
...	...	...	...	...	...	...	...
14830	25661	825	1086	16	2022	Kevin	Magnussen
14831	25662	848	1086	17	2022	Alexander	Albon
14832	25663	849	1086	18	2022	Nicholas	Latifi
14833	25664	852	1086	19	2022	Yuki	Tsunoda
14834	25665	822	1086	20	2022	Valtteri	Bottas

14835 rows × 7 columns

# reading file and making subsets for the required columns
pd.set_option('mode.chained_assignment', None) # remove warnings
lap_times = pd.read_csv("F1_data/lap_times.csv")
lap_times_subset = lap_times[['raceId','driverId', 'lap','milliseconds']]

# extracting the best driver based on the fastest lap time
pd.set_option('mode.chained_assignment', None) # remove warnings
lap_times_subset['rank'] = lap_times_subset.groupby(['raceId','driverId'])['milliseconds'].rank(method='first')
lap_times_filtered=lap_times_subset.loc[(lap_times_subset['rank'] == 1)]
lap_times_filtered

	raceId	driverId	lap	milliseconds	rank
43	841	20	44	89844	1.0
98	841	1	41	90314	1.0
165	841	17	50	89600	1.0
228	841	808	55	90064	1.0
286	841	13	55	88947	1.0
...	...	...	...	...	...
528498	1086	852	58	83538	1.0
528566	1086	855	58	82029	1.0
528628	1086	20	51	82824	1.0
528706	1086	849	60	82478	1.0
528773	1086	842	58	83199	1.0

9971 rows × 5 columns

# Filtering the Data to get only winners
Winners_data=Final_Dataset.loc[(Final_Dataset['positionText'] == 1)]
Winners_data = Winners_data.merge(lap_times_filtered, on=['raceId','driverId'], how='left')
display(Winners_data)

	resultId	driverId	raceId	positionText	year	forename	surname	lap	milliseconds	rank
0	1	1	18	1	2008	Lewis	Hamilton	39.0	87452.0	1.0
1	23	8	19	1	2008	Kimi	Räikkönen	37.0	95405.0	1.0
2	45	13	20	1	2008	Felipe	Massa	38.0	93600.0	1.0
3	67	8	21	1	2008	Kimi	Räikkönen	46.0	81670.0	1.0
4	89	13	22	1	2008	Felipe	Massa	16.0	86666.0	1.0
...	...	...	...	...	...	...	...	...	...	...
1068	25566	830	1082	1	2022	Max	Verstappen	64.0	75839.0	1.0
1069	25586	832	1083	1	2022	Carlos	Sainz	44.0	90813.0	1.0
1070	25606	844	1084	1	2022	Charles	Leclerc	62.0	67583.0	1.0
1071	25626	830	1085	1	2022	Max	Verstappen	30.0	97491.0	1.0
1072	25646	830	1086	1	2022	Max	Verstappen	45.0	82126.0	1.0

1073 rows × 10 columns

# Filtering data from 1996 onwards,since we have data for lap times starting 1996 
average_lap_times = pd.DataFrame(Winners_data.groupby(['year','driverId'], as_index = False)['milliseconds'].mean())
average_lap_times=average_lap_times.loc[(average_lap_times['year'] >= 1996)]
average_lap_times

	year	driverId	milliseconds
256	1996	30	101844.000000
257	1996	35	83689.500000
258	1996	44	85581.000000
259	1996	71	90738.000000
260	1997	14	88693.500000
...	...	...	...
387	2021	839	81421.000000
388	2022	815	76028.000000
389	2022	830	88567.625000
390	2022	832	90813.000000
391	2022	844	80804.333333

136 rows × 3 columns

# Summarizing data to get the number of win by each driver across all seasons
yearly_drivers_position = pd.DataFrame(Winners_data.groupby(['year','driverId'], as_index = False)['positionText'].count())
yearly_drivers_position=yearly_drivers_position.loc[(yearly_drivers_position['year'] >= 1996)]
yearly_drivers_position = yearly_drivers_position.rename(columns={'positionText':'races_won'})
display(yearly_drivers_position)
type(yearly_drivers_position)

	year	driverId	races_won
256	1996	30	3
257	1996	35	4
258	1996	44	1
259	1996	71	8
260	1997	14	2
...	...	...	...
387	2021	839	1
388	2022	815	1
389	2022	830	8
390	2022	832	1
391	2022	844	3

136 rows × 3 columns

pandas.core.frame.DataFrame

## joining the 2 datasets to create a final database for analysis
yearly_drivers_position_lap=pd.merge(yearly_drivers_position,average_lap_times,on=['year','driverId'],how='left')
yearly_drivers_position_lap

	year	driverId	races_won	milliseconds
0	1996	30	3	101844.000000
1	1996	35	4	83689.500000
2	1996	44	1	85581.000000
3	1996	71	8	90738.000000
4	1997	14	2	88693.500000
...	...	...	...	...
131	2021	839	1	81421.000000
132	2022	815	1	76028.000000
133	2022	830	8	88567.625000
134	2022	832	1	90813.000000
135	2022	844	3	80804.333333

136 rows × 4 columns

# filtering the data to only get the drivers with the maximum wins in a season
yearly_drivers_position_lap['count_max'] = yearly_drivers_position_lap.groupby(['year'])['races_won'].transform(max)
yearly_drivers_position2 = yearly_drivers_position_lap[(yearly_drivers_position_lap['races_won']==yearly_drivers_position_lap['count_max'])]
yearly_drivers_position2 = yearly_drivers_position2[['year','driverId','races_won','milliseconds']]
yearly_drivers_position2

	year	driverId	races_won	milliseconds
3	1996	71	8	90738.000000
6	1997	35	7	80679.857143
12	1998	57	8	87343.625000
18	1999	57	5	85398.200000
22	2000	30	9	86048.111111
26	2001	30	9	87799.222222
32	2002	30	11	83060.727273
39	2003	30	6	76803.000000
44	2004	30	13	81564.076923
46	2005	4	7	86675.571429
47	2005	8	7	85411.571429
51	2006	4	7	83043.285714
55	2006	30	7	83434.000000
58	2007	8	6	86817.500000
65	2008	13	6	89610.166667
70	2009	18	6	87486.166667
74	2010	4	5	99307.800000
77	2010	20	5	93325.800000
82	2011	20	11	94468.272727
89	2012	20	5	99101.800000
95	2013	20	13	95295.230769
96	2014	1	11	100653.090909
99	2015	1	10	96220.700000
102	2016	1	10	85645.500000
106	2017	1	9	92422.000000
111	2018	1	11	89360.818182
116	2019	1	11	86550.727273
121	2020	1	11	84877.545455
130	2021	830	10	92733.800000
133	2022	830	8	88567.625000

# extracting the best driver based on the fastest lap time
yearly_drivers_position2['rank'] = yearly_drivers_position2.groupby('year')['milliseconds'].rank(method='first')
yearly_drivers_position2=yearly_drivers_position2.loc[(yearly_drivers_position2['rank'] == 1)]
yearly_drivers_position2

	year	driverId	races_won	milliseconds	rank
3	1996	71	8	90738.000000	1.0
6	1997	35	7	80679.857143	1.0
12	1998	57	8	87343.625000	1.0
18	1999	57	5	85398.200000	1.0
22	2000	30	9	86048.111111	1.0
26	2001	30	9	87799.222222	1.0
32	2002	30	11	83060.727273	1.0
39	2003	30	6	76803.000000	1.0
44	2004	30	13	81564.076923	1.0
47	2005	8	7	85411.571429	1.0
51	2006	4	7	83043.285714	1.0
58	2007	8	6	86817.500000	1.0
65	2008	13	6	89610.166667	1.0
70	2009	18	6	87486.166667	1.0
77	2010	20	5	93325.800000	1.0
82	2011	20	11	94468.272727	1.0
89	2012	20	5	99101.800000	1.0
95	2013	20	13	95295.230769	1.0
96	2014	1	11	100653.090909	1.0
99	2015	1	10	96220.700000	1.0
102	2016	1	10	85645.500000	1.0
106	2017	1	9	92422.000000	1.0
111	2018	1	11	89360.818182	1.0
116	2019	1	11	86550.727273	1.0
121	2020	1	11	84877.545455	1.0
130	2021	830	10	92733.800000	1.0
133	2022	830	8	88567.625000	1.0

# joining the dataset to pull driver information into the final table
yearly_drivers_position3 = yearly_drivers_position2.merge(drivers_subset, on='driverId', how='left')
yearly_drivers_position3 = yearly_drivers_position3[['year','driverId','races_won','forename','surname']]
yearly_drivers_position3

	year	driverId	races_won	forename	surname
0	1996	71	8	Damon	Hill
1	1997	35	7	Jacques	Villeneuve
2	1998	57	8	Mika	Häkkinen
3	1999	57	5	Mika	Häkkinen
4	2000	30	9	Michael	Schumacher
5	2001	30	9	Michael	Schumacher
6	2002	30	11	Michael	Schumacher
7	2003	30	6	Michael	Schumacher
8	2004	30	13	Michael	Schumacher
9	2005	8	7	Kimi	Räikkönen
10	2006	4	7	Fernando	Alonso
11	2007	8	6	Kimi	Räikkönen
12	2008	13	6	Felipe	Massa
13	2009	18	6	Jenson	Button
14	2010	20	5	Sebastian	Vettel
15	2011	20	11	Sebastian	Vettel
16	2012	20	5	Sebastian	Vettel
17	2013	20	13	Sebastian	Vettel
18	2014	1	11	Lewis	Hamilton
19	2015	1	10	Lewis	Hamilton
20	2016	1	10	Lewis	Hamilton
21	2017	1	9	Lewis	Hamilton
22	2018	1	11	Lewis	Hamilton
23	2019	1	11	Lewis	Hamilton
24	2020	1	11	Lewis	Hamilton
25	2021	830	10	Max	Verstappen
26	2022	830	8	Max	Verstappen

# converting year from integer -> string
yearly_drivers_position3['year'] = yearly_drivers_position3['year'].astype(str)
yearly_drivers_position3.dtypes

year         object
driverId      int64
races_won     int64
forename     object
surname      object
dtype: object

import plotly.express as px
fig = px.bar(yearly_drivers_position3, x="year", y="races_won",facet_row_spacing = 1,facet_col_spacing=1,title='Maximun number of races won by a single driver across seasons')
fig.update_traces(width=0.5)
fig.update_traces(marker_color='blue')
fig.show()